This repository contains a collection of classic machine learning algorithms implemented from scratch using only the Python NumPy library.

Table of Contents

• About
• Algorithms
• To-Do
• Usage
• Contributing
• License

About

Machine Learning from Scratch is a project aimed at deepening our understanding of fundamental machine learning algorithms. By implementing these algorithms using only the NumPy library, we gain insights into the underlying mathematical concepts and optimization techniques.

Algorithms

Currently, the following algorithms have been implemented:

• k-Nearest Neighbors (knn.py)
• Linear Regression (linearRegression.py)
• Logistic Regression (logisticRegression.py)
• Naive Bayes (naiveBayes.py)
• Multi-Layer Perceptron (mlp.py)

Each algorithm is self-contained in its respective Python file.

To-Do

• Add comments to explain the equations used in each algorithm.
• Implement the Multi-Layer Perceptron (MLP) algorithm.

Usage

You can use these implementations to explore and experiment with the behavior of classic machine learning algorithms. To get started, follow these steps:

1. Clone the repository: git clone https://github.com/elcaiseri/Machine-Learning-from-Scratch.git
2. Install the required dependencies: pip install -r requirements.txt
3. Open the Python files corresponding to the algorithm of interest.
4. Run the Python scripts to observe the algorithm's behavior.

Contributing

We welcome contributions to improve existing implementations, add new algorithms, or fix issues. If you'd like to contribute, please follow the guidelines in CONTRIBUTING.md.

License

This project is licensed under the MIT License.

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© 2023 elcaiseri

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